DL-Art-School/codes/trainer/inject.py
James Betker 6084915af8 Support gaussian diffusion models
Adds support for GD models, courtesy of some maths from openai.

Also:
- Fixes requirement for eval{} even when it isn't being used
- Adds support for denormalizing an imagenet norm
2021-06-02 21:47:32 -06:00

65 lines
2.2 KiB
Python

import importlib
import inspect
import pkgutil
import re
import sys
import torch.nn
# Base class for all other injectors.
class Injector(torch.nn.Module):
def __init__(self, opt, env):
super(Injector, self).__init__()
self.opt = opt
self.env = env
if 'in' in opt.keys():
self.input = opt['in']
self.output = opt['out']
# This should return a dict of new state variables.
def forward(self, state):
raise NotImplementedError
def format_injector_name(name):
# Formats by converting from CamelCase to snake_case and removing trailing "_injector"
name = re.sub('(.)([A-Z][a-z]+)', r'\1_\2', name)
name = re.sub('([a-z0-9])([A-Z])', r'\1_\2', name).lower()
return name.replace("_injector", "")
# Works by loading all python modules in the injectors/ directory and sniffing out subclasses of Injector.
# field will be properly populated.
def find_registered_injectors(base_path="trainer/injectors"):
module_iter = pkgutil.walk_packages([base_path])
results = {}
for mod in module_iter:
if mod.ispkg:
EXCLUSION_LIST = []
if mod.name not in EXCLUSION_LIST:
results.update(find_registered_injectors(f'{base_path}/{mod.name}'))
else:
mod_name = f'{base_path}/{mod.name}'.replace('/', '.')
importlib.import_module(mod_name)
classes = inspect.getmembers(sys.modules[mod_name], inspect.isclass)
for name, obj in classes:
if 'Injector' in [mro.__name__ for mro in inspect.getmro(obj)]:
results[format_injector_name(name)] = obj
return results
class CreateInjectorError(Exception):
def __init__(self, name, available):
super().__init__(f'Could not find the specified injector name: {name}. Available injectors:'
f'{available}')
# Injectors are a way to synthesize data within a step that can then be used (and reused) by loss functions.
def create_injector(opt_inject, env):
injectors = find_registered_injectors()
type = opt_inject['type']
if type not in injectors.keys():
raise CreateInjectorError(type, list(injectors.keys()))
return injectors[opt_inject['type']](opt_inject, env)